Data Engineer

Technologies we use

About the project

This is how we organize our work

This is how we work

Your responsibilities

• Re-engineer our marketing data pipeline using a modern analytics stack, designing ingestion, dbt-based transformations, and Redshift warehouse models to replace today’s legacy SQL Server-centric processing.
• Take ownership of our production database estate, including performance, integrity, security, and cost management. Introduce CI/CD for database development so schema changes are version-controlled, tested, and deployed like code.
• Build automated data-quality processes and eliminate manual data movement, including migrating legacy SFTP workflows to AWS Transfer Family, so routine data operations run reliably without manual intervention.
• Build the data side of automated client onboarding, creating repeatable ingestion templates, validation, and mapping processes so new automotive clients can go live in hours rather than weeks.
• Use AI tooling such as Claude on a daily basis for query optimisation, pipeline debugging, and documentation, leveraging AI to increase your impact across a large and complex data estate.

Our requirements

• 3–5 years of hands-on experience building data pipelines and/or managing production databases. You have owned data platforms end-to-end, rather than simply writing queries against existing systems.
• Expert-level SQL skills with production experience in SQL Server (T-SQL) and at least one columnar data warehouse such as Redshift, Snowflake, or BigQuery. You understand query execution plans, indexing strategies, and warehouse-specific optimisation techniques.
• Hands-on experience with dbt or similar data transformation frameworks, Python for data engineering, and orchestration/ETL tools such as AWS Glue, Airflow, or Step Functions.
• Comfortable working with Terraform, CloudFormation, or CDK, as well as CI/CD pipelines. You treat databases and data pipelines as version-controlled, tested, and deployable artefacts.
• Proven experience migrating data workloads between platforms. You know how to execute migrations safely, including parallel runs, data reconciliation and validation checks, and well-planned cutovers that ensure no data is lost.
• Comfortable working in English with UK-based stakeholders, gathering knowledge from existing system owners, and documenting data pipelines and processes so that others can operate and maintain them effectively.